Chatbot conducting a virtual social dialogue

ABSTRACT

Improved techniques for dialogue management are disclosed. In particular, disclosed systems facilitate improved autonomous agents that can generate a virtual social dialogue from a corpus of text. A virtual social dialogue is a dialogue between autonomous agents and user devices. For example, a virtual social dialogue can include viewpoints, concerns, or questions of various actors on a particular topic. By presenting textual content in this manner, disclosed techniques improve information comprehension and increase the practicality of autonomous agents.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/804,977, filed Feb. 13, 2019, which is incorporated herein byreference in its entirety. Additional material can be found in U.S.patent application Ser. No. 16/789,849, filed Feb. 13, 2020, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

This disclosure is generally concerned with computational linguistics.More specifically, this disclosure relates to creating a virtual socialdialogue to facilitate an improved interaction with autonomous agents.

BACKGROUND

Linguistics is the scientific study of language. One aspect oflinguistics is the application of computer science to human naturallanguages such as English. Due to the greatly increased speed ofprocessors and capacity of memory, computer applications of linguisticsare on the rise.

For example, the use of autonomous agents to answer questions,facilitate discussion, manage dialogues, and provide social promotion isincreasingly popular. To address this need, a broad range oftechnologies has been developed. But such solutions are limited in themanner in which they can present information to a user. Hence, newsolutions are needed.

SUMMARY

Techniques are disclosed for dialogue management. In an example,disclosed techniques facilitate interactions between an autonomous agentand a user device, including providing a virtual social dialogue. Avirtual social dialogue is a multi-turn dialogue between imaginaryagents that is obtained as a result of content transformation. Contenttransformation can include translating an existing corpus of text intoquestions and answers that form the dialogue.

In an aspect, a method of dialogue management for an autonomous agentincludes receiving, from a user device, a search query including textfragments. The method further includes obtaining search results byperforming a search of electronic documents using the search query. Themethod further includes generating a syntactic similarity matrix thatnumerically represents a syntactic similarity between each of the searchresults. The method further includes generating a relevance similaritymatrix that numerically represents a relevancy between each of thesearch results. The method further includes clustering the searchresults into clusters by identifying pairs of the search results thatare separated in the syntactic similarity matrix by less than a firstminimum distance and are separated in the relevance similarity matrix byless than a second minimum distance. The method further includes forminga set of topics by identifying, for each cluster of the clusters, a nounthat is common between search results in the cluster. The method furtherincludes outputting the set of topics to the user device. The methodfurther includes receiving, from the user device, a selection of a topicfrom the set of topics. The method further includes identifying, fromthe electronic documents, a question and an answer that are relevant tothe selected topic. The answer is in rhetorical agreement with thequestion and the question and the answer form a virtual conversation.The method further includes providing the virtual conversation to theuser device.

In an aspect, generating the syntactic similarity matrix includesdetermining, for each search result of the search results, a distanceindicating similarity with each of the other search results. The firstminimum distance can be a minimum of the distances.

In a further aspect, generating the relevance similarity matrixincludes, for each search result of the search results identifying a setof keywords in the search result and calculating, for each keyword of aset of keywords, a respective frequency of occurrence. The secondminimum distance can be derived from the frequencies of occurrence.

In a further aspect, the clustering further includes iteratively, untila threshold number of clusters are obtained: identifying a first searchresult and a second search result that are separated by a minimumdistance and merging, into a cluster, the first search result and thesecond search result. The method further includes determining, for eachcluster, a topic including a noun phrase from a search result associatedwith the respective cluster.

In an aspect, the identifying includes operations. The operationsinclude constructing, from the electronic documents, a discourse tree.The operations further include identifying, from the discourse tree,satellite elementary discourse units. Each satellite elementarydiscourse unit represents an answer. The operations further includeidentifying a sentence corresponding to a satellite elementary discourseunit. The operations further include identifying, within the satelliteelementary discourse unit, a word that represents either (i) a noun,(ii) a verb, or (iii) adjective. The operations further includereplacing, in the sentence, the word with a question word, therebycreating a question. The identifying includes inserting the questionimmediately preceding the answer.

In an aspect, the method further includes responsive to receiving arequest to interact with the topics, from the user device, searching thetopics for a relevant fragment of text. The method further includesresponsive to determining that the relevant fragment of text isresponsive to the search query, presenting fragment of text to the userdevice.

In an aspect, the method further includes receiving, from a user device,an additional question including text fragments. The method furtherincludes generating, from the electronic documents, an additionalanswer. The additional answer is relevant to the topic. The additionalanswer is in rhetorical agreement with the additional question. Themethod further includes attributing an additional virtual actor to theadditional answer. The method further includes updating the virtualdialogue with the additional answer. The method further includesproviding the virtual dialogue to the user device.

In an aspect, the method further includes forming a virtual conversationfrom the question and the answer by attributing a first virtual actor tothe question and a second virtual actor to the answer.

The exemplary methods discussed above can be implemented on systemscomprising one or more processors or stored as instructions on anon-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a computing environment in accordance withan aspect of the present disclosure.

FIG. 2 depicts an example of a discourse tree in accordance with anaspect of the present disclosure.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect of the present disclosure.

FIG. 4 depicts illustrative schemas in accordance with an aspect of thepresent disclosure.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect of the present disclosure.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect of the present disclosure.

FIG. 7 depicts an exemplary discourse tree for an example request aboutproperty tax in accordance with an aspect of the present disclosure.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect of the present disclosure.

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect of the present disclosure.

FIG. 11 depicts an exemplary process for dialogue management using avirtual social dialogue, in accordance with an aspect of the presentdisclosure.

FIG. 12 depicts an exemplary user interface depicting a session using anautonomous agent, depicting conventional and virtual social dialogues,in accordance with an aspect of the present disclosure.

FIG. 13 depicts an exemplary process for clustering, in accordance withan aspect of the present disclosure.

FIG. 14 illustrates an example of a greedy search algorithm, inaccordance with an aspect of the present disclosure.

FIG. 15 illustrates an approach to Agglomerative Clustering, inaccordance with an aspect of the present disclosure.

FIG. 16 depicts an exemplary process for a construction of a virtualsocial dialogue, in accordance with an aspect of the present disclosure.

FIG. 17 illustrates an approach to virtual social dialogue construction,in accordance with an aspect.

FIG. 18 depicts a simplified diagram of a distributed system forimplementing one of the aspects.

FIG. 19 is a simplified block diagram of components of a systemenvironment by which services provided by the components of an aspectsystem may be offered as cloud services in accordance with an aspect.

FIG. 20 illustrates an exemplary computer system, in which variousaspects of the present invention may be implemented.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to autonomous agents(“chatbots”) that deliver content in the form of virtual dialogues thatare automatically produced from a corpus of text. Example of virtualdialogues are a virtual social dialogue and a virtual persuasivedialogue. A virtual social dialogue is a multi-step dialogue betweenimaginary agents and/or user devices and can be presented within aninteractive session between a user device and an autonomous agent. Avirtual persuasive dialogue is a multi-step adversarial argumentationdialogue between imaginary agents obtained as a result of contenttransformation.

Presentation of knowledge in dialogue format can be more effective thantraditional search-based techniques. For example, usability studies haveshown that for those acquiring information, dialogues often communicateinformation more effectively than monologue most of times. Chatbots canprovide users with a deep domain knowledge, personalization,interactivity and the level of understanding that can be lacking inmodern search engines. Chatbots can also implement social search,providing opinionated data from peers on request, performingpersonalization, and allow easy navigation through content.

In an example, an autonomous agent executing on a computing deviceaccesses an initial utterance from a user device. The utterance includesa search query, for example “mobile technology.” The agent locatesmultiple documents and determines topics based on the search query fromthe documents. Clustering can be used to group the determined topicsinto related clusters. Clustering can include greedy search and/oragglomerative clustering. Determined topics might include “what are thebenefits of this technology?” or “when will the technology be ready?”

Continuing the example, the agent presents the determined topics to theuser device. The user device can then make a selection of a desiredtopic, for example, “what are the benefits of this technology?” Uponreceiving a selection of a topic, the agent obtains a corpus of textsand creates a virtual social dialogue from the corpus of text. Thevirtual social dialogue includes questions and answers organized toappear as a dialogue between user devices and autonomous agents. Theagent presents the virtual social dialogue to the user device. Forexample, the dialogue might include “this technology can be leveraged bymobile devices,” “how can the technology be leveraged?” and “byproviding faster data downloads, thereby enabling new applications.” Theuser can continue to interact with the agent, for example, by requestingadditional information, asking the agent questions, or invoking anothervirtual social dialogue on a different topic.

Certain Definitions

As used herein, “rhetorical structure theory” is an area of research andstudy that provided a theoretical basis upon which the coherence of adiscourse could be analyzed.

As used herein, “discourse tree” or “DT” refers to a structure thatrepresents the rhetorical relations for a sentence of part of asentence.

As used herein, a “rhetorical relation,” “rhetorical relationship,” or“coherence relation” or “discourse relation” refers to how two segmentsof discourse are logically connected to one another. Examples ofrhetorical relations include elaboration, contrast, and attribution.

As used herein, a “sentence fragment,” or “fragment” is a part of asentence that can be divided from the rest of the sentence. A fragmentis an elementary discourse unit. For example, for the sentence “Dutchaccident investigators say that evidence points to pro-Russian rebels asbeing responsible for shooting down the plane,” two fragments are “Dutchaccident investigators say that evidence points to pro-Russian rebels”and “as being responsible for shooting down the plane.” A fragment can,but need not, include a verb.

As used herein, “signature” or “frame” refers to a property of a verb ina fragment. Each signature can include one or more thematic roles. Forexample, for the fragment “Dutch accident investigators say thatevidence points to pro-Russian rebels,” the verb is “say” and thesignature of this particular use of the verb “say” could be “agent verbtopic” where “investigators” is the agent and “evidence” is the topic.

As used herein, “thematic role” refers to components of a signature usedto describe a role of one or more words. Continuing the previousexample, “agent” and “topic” are thematic roles.

As used herein, “nuclearity” refers to which text segment, fragment, orspan, is more central to a writer's purpose. The nucleus is the morecentral span, and the satellite is the less central one.

As used herein, “coherency” refers to the linking together of tworhetorical relations.

As used herein, “communicative verb” is a verb that indicatescommunication. For example, the verb “deny” is a communicative verb.

As used herein, “communicative action” describes an action performed byone or more agents and the subjects of the agents.

Turning now to the Figures, FIG. 1 depicts an example of a computingenvironment in accordance with an aspect of the present disclosure. FIG.1 depicts one or more of computing device 101, display 130, network 150,user device 160, and external text corpus 170. In the example depictedin FIG. 1, computing device 101 communicates over network 150 with userdevice 160. Computing device 101 answers questions transmitted by userdevice 160 and as appropriate, generates and inserts a virtual socialdialogue into interactions between user device 160 and computing device101. User device 160 can be any mobile device such as a mobile phone,smart phone, tablet, laptop, smart watch, and the like.

Computing device 101 includes one or more of dialogue application 102,text corpus 105, classification model 120, and training data 125.Dialogue application 102 can interact with user device 160 by receivingquestions from user device 160 and answering those questions. In somecases, dialogue application 102 can facilitate a virtual social dialoguewith user device 160. An example of a process for facilitating virtualsocial dialogue is discussed further with respect to FIG. 11. Examplesof computing device 101 are distributed system 1800 and client computingdevices 1802, 1804, 1806, and 1808. Examples of user device 160 includeclient computing devices 1802, 1804, 1806, and 1808.

Computing device 101 can output interactions, e.g., questions andanswers, on display 130. User device 160 can also output interactions ona display. As depicted, display 130 includes various utterances. Forexample, dialogue application 102 asks a user a question via utterance131. In turn, the user responds with utterance 132 that he or she would“like to know more.” Dialogue application 102 outputs utterance 133,which states “Here is what people are saying about (2).” Dialogueapplication 102 then generates and outputs virtual social dialogue 134.Virtual social dialogue 134 includes utterances 135-137, which are shownas utterances between virtual users. For example, utterance 135 appearsto be from “User 1,” utterance 136 from “Agent 2,” and utterance 137from “user 2.” Utterances within a virtual social dialogue can appear tobe from an autonomous agent or a user.

To generate content for the virtual social dialogue 134, dialogueapplication 102 generates questions and answers from one or morecorpuses of text. For example, dialogue application 102 can use textcorpus 105, which can be local to computing device 101, and/or externaltext corpus 170, which is accessible via network 150. In an aspect, thegeneration of content can involve creating one or more communicativediscourse trees. In an aspect, dialogue application 102 can useclassification model 120 to determine rhetorical agreement betweensentences (e.g., questions and answers). Classification model 120 can betrained with training data 125. Classification model 120 can be trainedto identify rhetorical similarity between text. Classification model 120can be a predictive model, a classification model, or other modeltrained to detect a presence of particular features. An example of amodel is a support vector machine. For example, classification model 120can use one or more such models to analyze a communicative discoursetree. Examples of learning approaches include nearest neighbor modelsand tree kernel models. Examples of features that can be detectedinclude a presence of argumentation, rhetoric agreement, a consecutiveanswer, or a feature present in text.

Rhetoric Structure Theory and Discourse Trees

Linguistics is the scientific study of language. For example,linguistics can include the structure of a sentence (syntax), e.g.,subject-verb-object, the meaning of a sentence (semantics), e.g. dogbites man vs. man bites dog, and what speakers do in conversation, i.e.,discourse analysis or the analysis of language beyond the sentence.

The theoretical underpinnings of discourse, Rhetoric Structure Theory(RST), can be attributed to Mann, William and Thompson, Sandra,“Rhetorical structure theory: A Theory of Text organization,”Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243-281,1988. Similar to how the syntax and semantics of programming languagetheory helped enable modern software compilers, RST helped enabled theanalysis of discourse. More specifically RST posits structural blocks onat least two levels, a first level such as nuclearity and rhetoricalrelations, and a second level of structures or schemas. Discourseparsers or other computer software can parse text into a discourse tree.

Rhetoric Structure Theory models logical organization of text, astructure employed by a writer, relying on relations between parts oftext. RST simulates text coherence by forming a hierarchical, connectedstructure of texts via discourse trees. Rhetoric relations are splitinto the classes of coordinate and subordinate; these relations holdacross two or more text spans and therefore implement coherence. Thesetext spans are called elementary discourse units (EDUs). Clauses in asentence and sentences in a text are logically connected by the author.The meaning of a given sentence is related to that of the previous andthe following sentences. This logical relation between clauses is calledthe coherence structure of the text. RST is one of the most populartheories of discourse, being based on a tree-like discourse structure,discourse trees (DTs). The leaves of a DT correspond to EDUs, thecontiguous atomic text spans. Adjacent EDUs are connected by coherencerelations (e.g., Attribution, Sequence), forming higher-level discourseunits. These units are then also subject to this relation linking. EDUslinked by a relation are then differentiated based on their relativeimportance: nuclei are the core parts of the relation, while satellitesare peripheral ones. As discussed, in order to determine accuraterequest-response pairs, both topic and rhetorical agreement areanalyzed. When a speaker answers a question, such as a phrase or asentence, the speaker's answer should address the topic of thisquestion. In the case of an implicit formulation of a question, via aseed text of a message, an appropriate answer is expected not onlymaintain a topic, but also match the generalized epistemic state of thisseed.

Rhetoric Relations

Rhetorical relations can be described in different ways. For example,Mann and Thompson describe twenty-three possible relations. C. Mann,William & Thompson, Sandra. (1987) (“Mann and Thompson”). RhetoricalStructure Theory: A Theory of Text Organization. Other numbers ofrelations are possible.

TABLE 1 Relation Name Nucleus Satellite Antithesis ideas favored by theideas disfavored by the author author Background text whoseunderstanding text for facilitating is being facilitated understandingCircumstance text expressing the events an interpretive context or ideasoccurring in the of situation or time interpretive context Concessionsituation affirmed by situation which is author apparently inconsistentbut also affirmed by author Condition action or situation whoseconditioning situation occurrence results from the occurrence of theconditioning situation Elaboration basic information additionalinformation Enablement an action information intended to aid the readerin performing an action Evaluation a situation an evaluative commentabout the situation Evidence a claim information intended to increasethe reader's belief in the claim Interpretation a situation aninterpretation of the situation Justify text information supporting thewriter's right to express the text Motivation an action informationintended to increase the reader's desire to perform the action Non- asituation another situation which volitional causes that one, but notCause by anyone's deliberate action Non- a situation another situationwhich volitional is caused by that one, Result but not by anyone'sdeliberate action Otherwise action or situation whose conditioningsituation (anti occurrence results from conditional) the lack ofoccurrence of the conditioning situation Purpose an intended situationthe intent behind the situation Restatement a situation a reexpressionof the situation Solutionhood a situation or method a question, request,supporting full or partial problem, or other satisfaction of the needexpressed need Summary text a short summary of that text Volitional asituation another situation which Cause causes that one, by someone'sdeliberate action Volitional a situation another situation which Resultis caused by that one, by someone's deliberate action

Some empirical studies postulate that the majority of text is structuredusing nucleus-satellite relations. See Mann and Thompson. But otherrelations do not carry a definite selection of a nucleus. Examples ofsuch relations are shown below.

TABLE 2 Relation Name Span Other Span Contrast One alternate The otheralternate Joint (unconstrained) (unconstrained) List An item A next itemSequence An item A next item

FIG. 2 depicts an example of a discourse tree in accordance with anaspect of the present disclosure. FIG. 2 includes discourse tree 200.Discourse tree includes text span 201, text span 202, text span 203,relation 210 and relation 238. The numbers in FIG. 2 correspond to thethree text spans. FIG. 3 corresponds to the following example text withthree text spans numbered 1, 2, 3:

1. Honolulu, Hi. will be site of the 2017 Conference on Hawaiian History

2. It is expected that 200 historians from the U.S. and Asia will attend

3. The conference will be concerned with how the Polynesians sailed toHawaii

For example, relation 210, or elaboration, describes the relationshipbetween text span 201 and text span 202. Relation 238 depicts therelationship, elaboration, between text span 203 and 204. As depicted,text spans 202 and 203 elaborate further on text span 201. In the aboveexample, given a goal of notifying readers of a conference, text span 1is the nucleus. Text spans 2 and 3 provide more detail about theconference. In FIG. 2, a horizontal number, e.g., 1-3, 1, 2, 3 covers aspan of text (possibly made up of further spans); a vertical linesignals the nucleus or nuclei; and a curve represents a rhetoricrelation (elaboration) and the direction of the arrow points from thesatellite to the nucleus. If the text span only functions as a satelliteand not as a nuclei, then deleting the satellite would still leave acoherent text. If from FIG. 2 one deletes the nucleus, then text spans 2and 3 are difficult to understand.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect of the present disclosure. FIG. 3 includes components 301 and302, text spans 305-307, relation 310 and relation 328. Relation 310depicts the relationship, enablement, between components 306 and 305,and 307, and 305. FIG. 3 refers to the following text spans:

1. The new Tech Report abstracts are now in the journal area of thelibrary near the abridged dictionary.

2. Please sign your name by any means that you would be interested inseeing.

3. Last day for sign-ups is 31 May.

As can be seen, relation 328 depicts the relationship between entity 307and 306, which is enablement. FIG. 3 illustrates that while nuclei canbe nested, there exists only one most nuclear text span.

Constructing a Discourse Tree

Discourse trees can be generated using different methods. A simpleexample of a method to construct a DT bottom up is:

(1) Divide the discourse text into units by:

-   -   (a) Unit size may vary, depending on the goals of the analysis    -   (b) Typically, units are clauses

(2) Examine each unit, and its neighbors. Is there a relation holdingbetween them?

(3) If yes, then mark that relation.

(4) If not, the unit might be at the boundary of a higher-levelrelation. Look at relations holding between larger units (spans).

(5) Continue until all the units in the text are accounted for.

Mann and Thompson also describe the second level of building blockstructures called schemas applications. In RST, rhetoric relations arenot mapped directly onto texts; they are fitted onto structures calledschema applications, and these in turn are fitted to text. Schemaapplications are derived from simpler structures called schemas (asshown by FIG. 4). Each schema indicates how a particular unit of text isdecomposed into other smaller text units. A rhetorical structure tree orDT is a hierarchical system of schema applications. A schema applicationlinks a number of consecutive text spans, and creates a complex textspan, which can in turn be linked by a higher-level schema application.RST asserts that the structure of every coherent discourse can bedescribed by a single rhetorical structure tree, whose top schemacreates a span encompassing the whole discourse.

FIG. 4 depicts illustrative schemas in accordance with an aspect of thepresent disclosure. FIG. 4 shows a joint schema is a list of itemsconsisting of nuclei with no satellites. FIG. 4 depicts schemas 401-406.Schema 401 depicts a circumstance relation between text spans 410 and428. Scheme 402 depicts a sequence relation between text spans 420 and421 and a sequence relation between text spans 421 and 423. Schema 403depicts a contrast relation between text spans 430 and 431. Schema 404depicts a joint relationship between text spans 440 and 441. Schema 405depicts a motivation relationship between 450 and 451, and an enablementrelationship between 452 and 451. Schema 406 depicts joint relationshipbetween text spans 460 and 462. An example of a joint scheme is shown inFIG. 4 for the three text spans below:

1. Skies will be partly sunny in the New York metropolitan area today.

2. It will be more humid, with temperatures in the middle 80's.

3. Tonight will be mostly cloudy, with the low temperature between 65and 70.

While FIGS. 2-4 depict some graphical representations of a discoursetree, other representations are possible.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect of the present disclosure. As can beseen from FIG. 5, the leaves of a DT correspond to contiguousnon-overlapping text spans called Elementary Discourse Units (EDUs).Adjacent EDUs are connected by relations (e.g., elaboration, attribution. . . ) and form larger discourse units, which are also connected byrelations. “Discourse analysis in RST involves two sub-tasks: discoursesegmentation is the task of identifying the EDUs, and discourse parsingis the task of linking the discourse units into a labeled tree.” SeeJoty, Shafiq R and Giuseppe Carenini, Raymond T Ng, and Yashar Mehdad.2013. Combining intra- and multisentential rhetorical parsing fordocument-level discourse analysis. In ACL (1), pages 486-496.

FIG. 5 depicts text spans that are leaves, or terminal nodes, on thetree, each numbered in the order they appear in the full text, shown inFIG. 6. FIG. 5 includes tree 500. Tree 500 includes, for example, nodes501-507. The nodes indicate relationships. Nodes are non-terminal, suchas node 501, or terminal, such as nodes 502-507. As can be seen, nodes503 and 504 are related by a joint relationship. Nodes 502, 505, 506,and 508 are nuclei. The dotted lines indicate that the branch or textspan is a satellite. The relations are nodes in gray boxes.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect of the present disclosure. FIG. 6includes text 600 and text sequences 602-604. Text 600 is presented in amanner more amenable to computer programming. Text sequence 602corresponds to node 502, sequence 603 corresponds to node 503, andsequence 604 corresponds to node 504. In FIG. 6, “N” indicates a nucleusand “S” indicates a satellite.

Examples of Discourse Parsers

Automatic discourse segmentation can be performed with differentmethods. For example, given a sentence, a segmentation model identifiesthe boundaries of the composite elementary discourse units by predictingwhether a boundary should be inserted before each particular token inthe sentence. For example, one framework considers each token in thesentence sequentially and independently. In this framework, thesegmentation model scans the sentence token by token, and uses a binaryclassifier, such as a support vector machine or logistic regression, topredict whether it is appropriate to insert a boundary before the tokenbeing examined. In another example, the task is a sequential labelingproblem. Once text is segmented into elementary discourse units,sentence-level discourse parsing can be performed to construct thediscourse tree. Machine learning techniques can be used.

In one aspect of the present invention, two Rhetorical Structure Theory(RST) discourse parsers are used: CoreNLPProcessor which relies onconstituent syntax, and FastNLPProcessor which uses dependency syntax.See Surdeanu, Mihai & Hicks, Thomas & Antonio Valenzuela-Escarcega,Marco. Two Practical Rhetorical Structure Theory Parsers. (2015).

In addition, the above two discourse parsers, i.e., CoreNLPProcessor andFastNLPProcessor use Natural Language Processing (NLP) for syntacticparsing. For example, the Stanford CoreNLP gives the base forms ofwords, their parts of speech, whether they are names of companies,people, etc., normalize dates, times, and numeric quantities, mark upthe structure of sentences in terms of phrases and syntacticdependencies, indicate which noun phrases refer to the same entities.Practically, RST is a still theory that may work in many cases ofdiscourse, but in some cases, it may not work. There are many variablesincluding, but not limited to, what EDU's are in a coherent text, i.e.,what discourse segmenters are used, what relations inventory is used andwhat relations are selected for the EDUs, the corpus of documents usedfor training and testing, and even what parsers are used. So forexample, in Surdeanu, et al., “Two Practical Rhetorical Structure TheoryParsers,” paper cited above, tests must be run on a particular corpususing specialized metrics to determine which parser gives betterperformance. Thus unlike computer language parsers which givepredictable results, discourse parsers (and segmenters) can giveunpredictable results depending on the training and/or test text corpus.Thus, discourse trees are a mixture of the predicable arts (e.g.,compilers) and the unpredictable arts (e.g., like chemistry wereexperimentation is needed to determine what combinations will give youthe desired results).

In order to objectively determine how good a Discourse analysis is, aseries of metrics are being used, e.g., Precision/Recall/F1 metrics fromDaniel Marcu, “The Theory and Practice of Discourse Parsing andSummarization,” MIT Press, (2000). Precision, or positive predictivevalue is the fraction of relevant instances among the retrievedinstances, while recall (also known as sensitivity) is the fraction ofrelevant instances that have been retrieved over the total amount ofrelevant instances. Both precision and recall are therefore based on anunderstanding and measure of relevance. Suppose a computer program forrecognizing dogs in photographs identifies eight dogs in a picturecontaining 12 dogs and some cats. Of the eight dogs identified, fiveactually are dogs (true positives), while the rest are cats (falsepositives). The program's precision is ⅝ while its recall is 5/12. Whena search engine returns 30 pages only 20 of which were relevant whilefailing to return 40 additional relevant pages, its precision is 20/30=⅔while its recall is 20/60=⅓. Therefore, in this case, precision is ‘howuseful the search results are’, and recall is ‘how complete the resultsare.’” The F1 score (also F-score or F-measure) is a measure of a test'saccuracy. It considers both the precision and the recall of the test tocompute the score: F1=2×((precision×recall)/(precision+recall)) and isthe harmonic mean of precision and recall. The F1 score reaches its bestvalue at 1 (perfect precision and recall) and worst at 0.

Autonomous Agents or Chatbots

A conversation between Human A and Human B is a form of discourse. Forexample, applications exist such as FaceBook® Messenger, WhatsApp®,Slack,® SMS, etc., a conversation between A and B may typically be viamessages in addition to more traditional email and voice conversations.A chatbot (which may also be called intelligent bots or virtualassistant, etc.) is an “intelligent” machine that, for example, replaceshuman B and to various degrees mimics the conversation between twohumans. An example ultimate goal is that human A cannot tell whether Bis a human or a machine (the Turning test, developed by Alan Turing in1950). Discourse analysis, artificial intelligence, including machinelearning, and natural language processing, have made great stridestoward the long-term goal of passing the Turing test. Of course, withcomputers being more and more capable of searching and processing vastrepositories of data and performing complex analysis on the data toinclude predictive analysis, the long-term goal is the chatbot beinghuman-like and a computer combined.

For example, users can interact with the Intelligent Bots Platformthrough a conversational interaction. This interaction, also called theconversational user interface (UI), is a dialog between the end user andthe chatbot, just as between two human beings. It could be as simple asthe end user saying “Hello” to the chatbot and the chatbot respondingwith a “Hi” and asking the user how it can help, or it could be atransactional interaction in a banking chatbot, such as transferringmoney from one account to the other, or an informational interaction ina HR chatbot, such as checking for vacation balance, or asking an FAQ ina retail chatbot, such as how to handle returns. Natural languageprocessing (NLP) and machine learning (ML) algorithms combined withother approaches can be used to classify end user intent. An intent at ahigh level is what the end user would like to accomplish (e.g., getaccount balance, make a purchase). An intent is essentially, a mappingof customer input to a unit of work that the backend should perform.Therefore, based on the phrases uttered by the user in the chatbot,these are mapped that to a specific and discrete use case or unit ofwork, for e.g. check balance, transfer money and track spending are all“use cases” that the chatbot should support and be able to work outwhich unit of work should be triggered from the free text entry that theend user types in a natural language.

The underlying rational for having an AI chatbot respond like a human isthat the human brain can formulate and understand the request and thengive a good response to the human request much better than a machine.Thus, there should be significant improvement in the request/response ofa chatbot, if human B is mimicked. So an initial part of the problem ishow does the human brain formulate and understand the request? To mimic,a model is used. RST and DT allow a formal and repeatable way of doingthis.

At a high level, there are typically two types of requests: (1) Arequest to perform some action; and (2) a request for information, e.g.,a question. The first type has a response in which a unit of work iscreated. The second type has a response that is, e.g., a good answer, tothe question. The answer could take the form of, for example, in someaspects, the AI constructing an answer from its extensive knowledgebase(s) or from matching the best existing answer from searching theinternet or intranet or other publically/privately available datasources.

Discourse Trees

More specifically, to represent linguistic features of text, certainaspects described herein use rhetoric relations and speech acts (orcommunicative actions). Rhetoric relations are relationships between theparts of the sentences, typically obtained from a discourse tree. Speechacts are obtained as verbs from a verb resource such as VerbNet. Byusing both rhetoric relations and communicative actions, aspectsdescribed herein can correctly recognize valid request-response pairs.To do so, aspects correlate the syntactic structure of a question withthat of an answer. By using the structure, a better answer can bedetermined.

For example, when an autonomous agent receives an indication from aperson that the person desires to sell an item with certain features,the autonomous agent should provide a search result that not onlycontains the features but also indicates an intent to buy. In thismanner, the autonomous agent has determined the user's intent.Similarly, when an autonomous agent receives a request from a person toshare knowledge about a particular item, the search result shouldcontain an intent to receive a recommendation. When a person asks anautonomous agent for an opinion about a subject, the autonomous agentshares an opinion about the subject, rather than soliciting anotheropinion.

Analyzing Request and Response Pairs

FIG. 7 depicts an exemplary discourse tree for an example request aboutproperty tax in accordance with an aspect of the present disclosure. Thenode labels are the relations and the arrowed line points to thesatellite. The nucleus is a solid line. FIG. 7 depicts the followingtext.

Request: “My husbands' grandmother gave him his grandfather's truck. Shesigned the title over but due to my husband having unpaid fines on hislicense, he was not able to get the truck put in his name. I wanted toput in my name and paid the property tax and got insurance for thetruck. By the time it came to sending off the title and getting the tag,I didn't have the money to do so. Now, due to circumstances, I am notgoing to be able to afford the truck. I went to the insurance place andwas refused a refund. I am just wondering that since I am not going tohave a tag on this truck, is it possible to get the property taxrefunded?”

Response: “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax. If you apply late, therewill be penalties on top of the normal taxes and fees. You don't need toregister it at the same time, but you absolutely need to title it withinthe period of time stipulated in state law.”

As can be seen in FIG. 7, analyzing the above text results in thefollowing. “My husbands' grandmother gave him his grandfather's truck”is elaborated by “She signed the title over but due to my husband”elaborated by “having unpaid fines on his license, he was not able toget the truck put in his name.” which is elaborated by “I wanted to putin my name,” “and paid the property tax”, and “and got insurance for thetruck.”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck.” iselaborated by;

“I didn't have the money” elaborated by “to do so” contrasted with

“By the time” elaborated by “it came to sending off the title”

“and getting the tag”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so” is contrasted with

“Now, due to circumstances,” elaborated with “I am not going to be ableto afford the truck.” which is elaborated with

“I went to the insurance place”

“and was refused a refund”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so. Now, due to circumstances, I am not going to be ableto afford the truck. I went to the insurance place and was refused arefund.” is elaborated with

“I am just wondering that since I am not going to have a tag on thistruck, is it possible to get the property tax refunded?”

“I am just wondering” has attribution to

“that” is the same unit as “is it possible to get the property taxrefunded?” which has condition “since I am not going to have a tag onthis truck”

As can be seen, the main subject of the topic is “Property tax on acar”. The question includes the contradiction: on one hand, allproperties are taxable, and on the other hand, the ownership is somewhatincomplete. A good response has to address both topic of the questionand clarify the inconsistency. To do that, the responder is making evenstronger claim concerning the necessity to pay tax on whatever is ownedirrespectively of the registration status. This example is a member ofpositive training set from our Yahoo! Answers evaluation domain. Themain subject of the topic is “Property tax on a car”. The questionincludes the contradiction: on one hand, all properties are taxable, andon the other hand, the ownership is somewhat incomplete. A goodanswer/response has to address both topic of the question and clarifythe inconsistency. The reader can observe that since the questionincludes rhetoric relation of contrast, the answer has to match it witha similar relation to be convincing. Otherwise, this answer would lookincomplete even to those who are not domain experts.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7, according to certain aspects of the present invention. Thecentral nucleus is “the property tax is assessed on property” elaboratedby “that you own”. “The property tax is assessed on property that youown” is also a nucleus elaborated by “Just because you chose to notregister it does not mean that you don't own it, so the tax is notrefundable. Even if you have not titled the vehicle yet, you still ownit within the boundaries of the tax district, so the tax is payable.Note that all states give you a limited amount of time to transfer titleand pay the use tax.”

The nucleus “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax.” is elaborated by “therewill be penalties on top of the normal taxes and fees” with condition“If you apply late,” which in turn is elaborated by the contrast of “butyou absolutely need to title it within the period of time stipulated instate law.” and “You don't need to register it at the same time.”

Comparing the DT of FIG. 7 and DT of FIG. 8, enables a determination ofhow well matched the response (FIG. 8) is to the request (FIG. 7). Insome aspects of the present invention, the above framework is used, atleast in part, to determine the DTs for the request/response and therhetoric agreement between the DTs.

In another example, the question “What does The Investigative Committeeof the Russian Federation do” has at least two answers, for example, anofficial answer or an actual answer.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect of the present disclosure. As depicted in FIG. 9, anofficial answer, or mission statement states that “The InvestigativeCommittee of the Russian Federation is the main federal investigatingauthority which operates as Russia's Anti-corruption agency and hasstatutory responsibility for inspecting the police forces, combatingpolice corruption and police misconduct, is responsible for conductinginvestigations into local authorities and federal governmental bodies.”

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect of the present disclosure. As depicted in FIG. 10, another,perhaps more honest, answer states that “Investigative Committee of theRussian Federation is supposed to fight corruption. However, top-rankofficers of the Investigative Committee of the Russian Federation arecharged with creation of a criminal community. Not only that, but theirinvolvement in large bribes, money laundering, obstruction of justice,abuse of power, extortion, and racketeering has been reported. Due tothe activities of these officers, dozens of high-profile cases includingthe ones against criminal lords had been ultimately ruined.”

The choice of answers depends on context. Rhetoric structure allowsdifferentiating between “official”, “politically correct”,template-based answers and “actual”, “raw”, “reports from the field”, or“controversial” answers, see FIGS. 9 and 10). Sometimes, the questionitself can give a hint about which category of answers is expected. If aquestion is formulated as a factoid or definitional one, without asecond meaning, then the first category of answers is suitable.Otherwise, if a question has the meaning “tell me what it really is”,then the second category is appropriate. In general, after extracting arhetoric structure from a question, selecting a suitable answer thatwould have a similar, matching, or complementary rhetoric structure iseasier.

The official answer is based on elaboration and joints, which areneutral in terms of controversy a text might contain (See FIG. 9). Atthe same time, the raw answer includes the contrast relation. Thisrelation is extracted between the phrase for what an agent is expectedto do and what this agent was discovered to have done.

Communicative Discourse Trees (CDTs)

Dialogue application 102 can create, analyze, and compare communicativediscourse trees. Communicative discourse trees are designed to combinerhetoric information with speech act structures. CDTs include with arcslabeled with expressions for communicative actions. By combiningcommunicative actions, CDTs enable the modeling of RST relations andcommunicative actions. A CDT is a reduction of a parse thicket. SeeGalitsky, B, Ilvovsky, D. and Kuznetsov S O. Rhetoric Map of an Answerto Compound Queries Knowledge Trail Inc. ACL 2015, 681-686. (“Galitsky2015”). A parse thicket is a combination of parse trees for sentenceswith discourse-level relationships between words and parts of thesentence in one graph. By incorporating labels that identify speechactions, learning of communicative discourse trees can occur over aricher features set than just rhetoric relations and syntax ofelementary discourse units (EDUs).

An exemplary process for building a communicative discourse tree isdescribed below. Dialogue application 102 accesses a sentence comprisingfragments. At least one fragment includes a verb and words and each wordincludes a role of the words within the fragment, and each fragment isan elementary discourse unit. For example, discourse application 102accesses a sentence such as “Rebels, the self-proclaimed DonetskPeople's Republic, deny that they controlled the territory from whichthe missile was allegedly fired.”

Continuing the example, dialogue application 102 determines that thesentence includes several fragments. For example, a first fragment is“rebels . . . deny.” A second fragment is “that they controlled theterritory.” A third fragment is “from which the missile was allegedlyfired.” Each fragment includes a verb, for example, “deny” for the firstfragment and “controlled” for the second fragment. Although, a fragmentneed not include a verb.

Dialogue application 102 generates a discourse tree that representsrhetorical relationships between the sentence fragments. The discoursetree includes nodes. Each nonterminal node represents a rhetoricalrelationship between two of the sentence fragments and each terminalnode of the nodes of the discourse tree is associated with one of thesentence fragments.

Continuing the example, dialogue application 102 generates a discoursetree. For example, referring back to the text above, the third fragment,“from which the missile was allegedly fired” elaborates on “that theycontrolled the territory.” The second and third fragments togetherrelate to attribution of what happened, i.e., the attack cannot havebeen the rebels because they do not control the territory.

Dialogue application 102 accesses multiple verb signatures. Continuingthe example, dialogue application 102 accesses a list of verbs, e.g.,from VerbNet. Each verb matches or is related to the verb of thefragment. For example, the for the first fragment, the verb is “deny.”Accordingly, dialogue application 102 accesses a list of verb signaturesthat relate to the verb deny.

Each verb signature includes the verb of the fragment and one or more ofthematic roles. For example, a signature includes one or more of nounphrase (NP), noun (N), communicative action (V), verb phrase (VP), oradverb (ADV). The thematic roles describing the relationship between theverb and related words. For example “the teacher amused the children”has a different signature from “small children amuse quickly.” For thefirst fragment, the verb “deny,” discourse application 102 accesses alist of frames, or verb signatures for verbs that match “deny.” The listis “NP V NP to be NP,” “NP V that S” and “NP V NP.”

Each verb signature includes thematic roles. A thematic role refers tothe role of the verb in the sentence fragment. Dialogue application 102determines the thematic roles in each verb signature. Example thematicroles include actor, agent, asset, attribute, beneficiary, cause,location destination source, destination, source, location, experiencer,extent, instrument, material and product, material, product, patient,predicate, recipient, stimulus, theme, time, or topic.

Dialogue application 102 determines, for each verb signature of the verbsignatures, a number of thematic roles of the respective signature thatmatch a role of a word in the fragment. For the first fragment, dialogueapplication 102 determines that the verb “deny” has only three roles,“agent”, “verb” and “theme.”

Dialogue application selects a particular verb signature from the verbsignatures based on the particular verb signature having a highestnumber of matches. For example, referring again to the text above, denyin the first fragment “the rebels deny . . . that they control theterritory” is matched to verb signature deny “NP V NP”, and “control” ismatched to control (rebel, territory). Verb signatures are nested,resulting in a nested signature of “deny(rebel, control(rebel,territory)).”

Dialogue Management Using a Virtual Social Dialogue

Aspects of the present disclosure relate to autonomous agents (chatbots)that deliver content in the form of virtual social dialogues that areautomatically produced from textual documents. A virtual social dialoguecan be presented as part of an interaction between user and agent.

Dialogue management, which can be performed by dialogue application 102,includes processing clarification requests and hints received from theuser device (e.g., an indication that a user is further interested in aspecific topic or item of content). Once a current answer is deliveredto the user device, the agent can ask whether the user is happy with theanswer provided. The agent can suggest options for further interactions,for example, a more traditional question and answer approach or avirtual social dialogue.

FIG. 11 depicts a process 1100 for dialogue management using a virtualsocial dialogue, in accordance with an aspect of the present disclosure.FIG. 11 can be implemented by dialogue application 102. For illustrativepurposes, FIG. 11 is discussed in conjunction with FIG. 12.

FIG. 12 depicts an exemplary user interface depicting a session using anautonomous agent, depicting conventional and virtual social dialogues,in accordance with an aspect of the present disclosure. FIG. 12 depictsdialogue session 1200. Dialogue session 1200 includes utterances1201-1210 and virtual social dialogue sessions 1220 and 1230.

In the example depicted in FIG. 12, dialogue application 102,implementing an autonomous agent, interacts with user device 160.Dialogue session 1200 includes two virtual social dialogues 1220 and1230, but fewer or more virtual social dialogue sessions are possible.Virtual social dialogue 1200 is merely an example; other dialoguesessions can differ. Dialogue application 102 initiates the session byoutputting utterance 1201, which states “ask a new question.”

At block 1101, process 1100 involves receiving, from a user device, asearch query including text fragments. Continuing the example, dialogueapplication 102 receives the user utterance 1202, which states“advantages and new features of 5G.” A user utterance can be in the formof a sentence, question, or simply a few words.

At block 1102, process 1100 involves obtaining search results byperforming a search of electronic documents using the search query.Continuing the example, dialogue application 102 searches electronicdocuments, for example, text corpus 105 or external text corpus 170.Dialogue application 102 can use any standard search techniques tolocate relevant electronic documents. For example, keyword-basedsearching can be employed. In this case, the search results can includeresults that included a threshold level of keyword matches with thesearch query.

In some cases, dialogue application 102 outputs a status, such asutterance 1203. As can be seen, utterance 1203 lists some universalresource locators (URLs) which dialogue application 102 analyzes inresponse to user utterance 1202. Continuing the example, dialogueapplication 102 retrieves search results based on the search query.

At block 1103, process 1100 involves forming a set of topics byclustering the search results. Clustering involves determining a numberof topics from the search results by grouping semantically similarand/or relevant search results together into a topic. Continuing theexample, at block 1103, dialogue application 102 forms a set of topicsfrom the search results by using clustering. Clustering is describedfurther with respect to FIG. 13. From the clustering, dialogueapplication 102 obtains a set of topics.

At block 1104, process 1100 involves outputting, to the user device, theset of topics. Continuing the example, dialogue application 102 outputsutterance 1204, which includes the set of determined topics from block1103. Utterance 1204 lists options as “demonstrating the benefits of thetechnology[1],” “wide range of people from student [2],” “next wirelessnetwork[2]. are already being built,” “5g-ready[3],” “5g new radio nrspecification [3]” and “next generation mobile networks alliance[9].” Insome cases, as can be seen, dialogue application 102 asks forclarification, for example “I believe these are the main topics of yourquery: is this what you meant?”

At block 1105, process 1100 involves receiving, from the user device, aselection of a topic from the set of topics. Continuing the example, auser device can transmit a selection to dialogue application 102. Theuser device selects “next state in technology (or [9]),” as depicted inutterance 1205. Continuing the example, dialogue application 102 outputsutterance 1206, which states “Put simply, it's the next stage in mobiletechnology. It follows 4G and 4G LTE and builds on what they offer,delivering everything at a faster speed . . . .”

In the example shown, dialogue application 102 also asks the user devicefor further clarification, for example “Are you OK with this answer?yes/more/no/specify [different topic]/reduce search to webdomain/virtual social dialogue.” Dialogue application 102 can presentdifferent options to the user. Examples of options include accepting theanswer and concluding the session, navigating to another answer,rejecting the answer, and reformulating the query, narrowing searchresults to a particular domain, e.g., quota.com, and proceeding toobtain more search results in the form of a virtual social dialogue. Ascan be seen by utterance 1207, the user device requests a “virtualsocial dialogue.”

At block 1106, process 1100 involves constructing a virtual socialdialogue. An example of a process of constructing a virtual socialdialogue is discussed further with respect to FIG. 16. For example,dialogue application 102 identifies, from the electronic documents, oneor more pairs of questions and answers that are relevant to the selectedtopic. Each question and the answer form a virtual conversation.

In an aspect, dialogue application 102 can screen questions and answersto ensure that the questions and answers are in rhetorical agreementwith each other and/or with the other questions and answers in thevirtual social dialogue. Classification model 120 can be trained andused for this purpose.

At block 1107, process 1100 involves providing the virtual conversationto the user device. Continuing the example, dialogue application 102outputs utterance 1208, which indicates to the user that a virtualsocial dialogue follows. Dialogue application 102 outputs virtual socialdialogue 1220.

As can be seen, the virtual social dialogue 1220 appears as aconversation between imaginary users (“User1” and “User2”) and a chatbot(“Agent1”). But any number of users or chatbots can be depicted. Thetopic of the utterances by the users and chatbots remains consistentwith the original query. As long as imaginary chatbot responds to thesame person, the dialog is intended to stay cohesive; coreferences inthe follow-up questions are maintained.

Further, as depicted, the virtual social dialogue 1220 is shown inframes to draw a visual distinction. The primary dialogue can be viewedas a one in the meta-level, and the object-level dialogue is naturallyembedded into the meta-level one.

Continuing the example, a virtual social dialogue can be used one ormore times during a dialogue session. For example, as depicted, inresponse to virtual social dialogue 1220, as indicated by utterance1209, the user asks “Are these features right for me?” In response,dialogue application 102 outputs utterance 1210, which states “This iswhat has been answered to people with similar questions.” Dialogueapplication 102 then outputs virtual social dialogue 1230.

Dialogue application 102 can continue to interact with user device 160as necessary. For example, the user device 160 can navigate throughdifferent topics, optionally using virtual social dialogues for thetopics.

Clustering

When search queries are formed that express a broad user intent,frequently, fairly large result sets are returned, which can pose aproblem for navigation. Clustering can address this problem. Clusteringinvolves grouping search results into semantically similar results(possibly in real-time), and presenting descriptive summaries of thesegroups to a user. In some cases, clustering allows a user to identify auseful subset of the results, which can be provided as input as arefinement into a clustering algorithm, thereby identifying narrowersubsets. Narrower subsets can be easier to navigate. These narrowersubsets can be narrowed further.

To be useful, clusters of search results should meet some basiccriteria. Firstly, each cluster should be associated with a meaningcommunicated with the user (by labels, snippets or individual searchresults indicative of this cluster). Secondly, search results of thesame cluster should have a similarity with each other. Each clusterneeds to be a coherent subset of possible search intents. Thirdly,search results assigned to different clusters should be substantiallydifferent from one another. Each cluster needs to contain a distinctsubset of search intents.

For example, a clustering algorithm should implement clustering as aclassification of a document into a cluster. Documents can be treated asvectors of weight-term pairs. The system designer needs to decide onwhich terms to choose and whether to use the whole document or only apart of it as the source of terms. The classification algorithm shouldbe selected. The existing clustering techniques vary in accuracy,robustness, speed and storage requirements. The output of theclassifier, or cluster representations, should be determined. Theclassification process results in a set of clusters, where every clustercontains documents about a unique topic. Clusters can be representedusing a selected document or term list, and more creativity with clusterrepresentation is needed. A set of evaluation criteria should bedeveloped. After the classification tool is created, the results need tobe analyzed and performance evaluated from the effectiveness andefficiency viewpoint. Evaluation can be difficult in some cases.

Different clustering methods can be used. Primary differences betweenclustering approaches involve defining the similarity function,adjusting the clustering algorithm, and producing informative snippetsfor the obtained clusters. Traditional clustering approaches involveembedding documents into vectors and then computing a geometric functionon them, such as cosine, to measuring their similarity. While suchapproaches have a solid theoretical foundation, the results arefrequently random and illogical, highly subject to the peculiarities ofthe documents being clustered.

In an aspect, hierarchical clustering algorithms can also be used.Hierarchical clustering algorithms are either top-down or bottom-up. Theformer class of algorithms tackles each document as a singleton clusterat the outset and then successively merge (or agglomerate) pairs ofclusters until all clusters have been merged into a single cluster thatcontains all documents. Bottom-up hierarchical clustering is thereforecalled hierarchical agglomerative clustering. Top-down clusteringrequires a method for splitting a cluster, doing it recursively untilindividual documents are reached. An example of clustering approachesare discussed with respect to FIGS. 13-15.

FIG. 13 depicts an exemplary process 1300 for clustering, in accordancewith an aspect of the present disclosure. As discussed with respect toFIG. 11, clustering can be used to determine topics from search resultsobtained from queries of electronic documents. Process 1300 can beimplemented by dialogue application 102.

Generally, clustering involves grouping two objects together that have asimilarity that is less than a threshold amount, or within a tolerance.For example, each object can be represented by a vector. In the case ofobjects that are text (e.g., a sentence), the vector can represent adistribution of words (e.g., a histogram). Given a numericalrepresentation, a difference between two fragments of text (e.g., twosentences or utterances) can be quantified.

Dialogue application 102 can cluster text based on syntactic similarityand/or relevance. In some cases, clustering of text can involvecomparing both syntactic similarity, e.g., a similarity of the meaningof the objects. For example, consider the example phrases “cellularphone,” “mobile phone,” “5G [fifth generation cellular] technology,”“base station,” and “Windows 10.”

At block 1301, process 1300 involves generating a syntactic similaritymatrix that numerically represents a syntactic similarity between eachof the search results. Table 3, below, depicts an example of a syntacticsimilarity matrix. In table 3, the numbers indicate distance. Forexample, a “1” indicates high distance (and therefore lower syntacticsimilarity), whereas a “0” indicates lower distance (and therefore highsyntactic similarity). As can be seen, the object “cellular phone” has ahigh syntactic similarity with “mobile phone” as these objects refer tothe same thing.

TABLE 3 Object 1 Object 2 Object 3 Object 4 Object 5 Object 1 X 0 1 1 1“cellular phone” Object 2 0 X 1 1 1 “mobile phone” Object 3 1 1 X 1 1“5G technology” Object 4 1 1 1 X 1 “base station” Object 5 1 1 1 1 1“Windows 10”

At block 1302, process 1300 involves generating a relevance similaritymatrix that numerically represents a relevancy between each of thesearch results. Clustering can also involve a relevance similarity,e.g., how relevant a first object is to a second object. In the tablebelow, the numbers indicate relevance distance. For example, a “1”indicates high distance (and therefore lower relevancy), whereas a “0”indicates lower distance (and therefore higher relevancy).

Continuing the example, table 4, below depicts an example of a relevancesimilarity matrix. Table 4 lists objects “cellular phone,” “mobilephone,” “5G technology, “base station” which are relevant to each other,as reflected in a relevance distance of zero. Table 4 also lists “5Gtechnology,” which has a relevance distance of 0.1 from “cellularphone,” and “mobile phone.” But as can be seen, “Windows 10” is notrelevant to any other object, thereby having a relevance distance of 1.

TABLE 4 Object 1 Object 2 Object 3 Object 4 Object 5 Object 1 X 0 0 0 1“cellular phone” Object 2 0 X 0 0 1 “mobile phone” Object 3 0.1 0.1 X0.1 1 “5G technology” Object 4 0 0 0 X 1 “base station” Object 5 1 1 1 11 “Windows 10”

At block 1303, process 1300 involves clustering the search results intoclusters by identifying pairs of the search results that (i) areseparated in the syntactic similarity matrix by less than a firstminimum distance and (ii) are separated in the relevance similaritymatrix by less than a second minimum distance.

Continuing the example, if a second distance (relevance) is less than0.2, then the objects “cellular phone,” “mobile phone,” “5G technology,”and “base station” are grouped together but “Windows 10” is not.Therefore, at block 1303, two clusters are formed. The first clusterincludes “cellular phone,” “mobile phone,” “5G technology,” and “basestation.” The second cluster includes “Windows 10.”

At block 1304, process 1300 involves forming a set of topics byidentifying, for each cluster of the clusters, a noun phrase from one ormore search results in the cluster. Continuing the example, the firstcluster might be named “cellular,” from a word extracted from “cellularphone.” The second cluster might be named “Windows.” In some cases, thenoun phrase occurs in all search results associated with the clusterand/or occupies a position in a title, top-level nucleus (of a discoursetree), abstract, or keyword of the respective search result. In thismanner, an importance of the noun to the rest of the text associatedwith the search result.

A Greedy Search Algorithm

In an example, a greedy search algorithm is used as part of a clusteringapproach. One example is depicted in FIG. 14.

FIG. 14 illustrates an example of a greedy search algorithm, inaccordance with an aspect of the present disclosure. FIG. 14 depictsgreedy search algorithm, which includes operations 1401-1433.

The input of the algorithm is a user query q in NL and a subset ofsnippets A*_(last) ranked by their relevance for the last successfulrefined query, each snippet a∈A*_(last) has a particular real-valuedweight w∈R. These weights are assigned to snippets by a search engineand reflect not only relevance to the query, but also might take intoaccount the user's profile, item popularity, geo-location, his searchhistory, etc. The input at the initial call is a user query q and theempty set of snippets A*_(last).

At the first step (line 1) the request is sent to a search engine. Then,a function 6 is applied to the set of returned snippets A and therequest q in order to obtain their unique formal representations δ(q)and A_(δ)={δ(a)|a∈A}, respectively. This representation makes textscomparable to each other.

To compute clusters (operation 1404) of similar snippets we use twomatrices: the matrix of syntactic similarity S and search relevancesimilarity matrix W with the entries

s_(ij)=sim(δ(a_(i)),δ(a_(j))), i,j=1, . . . , |A| and

w_(ij)=rel_sim(w_(i),w_(j)), i,j=1, . . . , |A|, respectively.

Values of both similarity matrices can be scaled to [0,1]. Centroids ofthe computed clusters C are the candidates for a new refined request.Specific information about the clusters is being presented to the useruntil a cluster with relevant specification is found (operations1407-1422).

In some cases, a user can further refine the approach. In an example,the interaction with the user is carried out in 4 steps:

-   -   1) The biggest clusters C is chosen, i.e., C=argmax_(C)∈        {δ(a)|δ(a)∈C} (line 8);    -   2) The added information in C w.r.t. q is computed. In can be        done formally by computing the difference between a centroid of        cluster C and δ(q) (see ComputeDifference function, line 9);    -   3) The computed difference is translated into a set of phrases        ;    -   4)        is shown to the user and feedback r∈{ShowDetails, Relevant,        Irrelevant} is received.

The feedback defines the further strategy of the chatbot.

ShowDetails means that the user has found the information he or shesearched for and all the snippets/documents corresponding to the clusterwill be returned to the user ranked by their relevance weights(operation 1425) assigned by the search engine. Relevant answer is thecase where the user has found a proposed query specification quiteuseful, but not enough (i.e., the further query specification isrequired), in this case a new augmented query q_(aug) is sent to thesearch engine (operation 1427) via the recursive call ofGreedySearch(q_(aug), A*), Irrelevant answer describes the case wherespecifications do not contain relevant information. When all proposedspecifications in

are irrelevant, the algorithm returns a subset of snippets from acluster with the last relevant specification (operation 1431).

Agglomerative Clustering Algorithm

An example of a clustering algorithm is agglomerative clustering.Agglomerative clustering can be applied to the search queries such asthose generated at block 1101 of process 1000. Termination criteriaensure that each centroid of clusters (i.e., the shared information ofsnippets in a cluster) will be the shortest specification of therequest.

FIG. 15 illustrates an approach to Agglomerative Clustering, inaccordance with an aspect of the present disclosure. FIG. 15 depictsagglomerative clustering algorithm 1500, which includes operations1501-1514.

In agglomerative clustering algorithm 1500, a cluster is denoted bycapital letter C and the corresponding centroid by lower case letter c.For the sake of convenience some functions are defined:

Input: query δ(q), snippet set A_(δ)

Output: set of subsets of snippets{A*|A*⊆A}=AgglomerativeClustering(δ(q),A_(δ))

As mentioned above, requests and snippets are given in NL. We define amapping δ: L→V that maps a text in natural language to a unique formalrepresentation, L is a space of all possible texts in natural language,V is a space of their formal representations. Further we consider theexamples of spaces V and discuss how the functions defined in thissection can be rewritten for the considered spaces.

sim: V×V→[0,1]⊂R is a function that evaluates similarity between twoobjects, the similarity between an object and its copy is equal to 1.

merge: V×V→V is a function that returns a shared description of its twoarguments, the shared description is in the same space as the mergedarguments. is_included: V×V→{True, False} is a function that returnsTrue if the description of the first argument is included in thedescription of the second one, False otherwise.

rel_sim: R×R→[0, 1]⊂R is a function that evaluates relevance similaritybetween two objects by their relevance weights, the similarity betweenan object and its copy is equal to 1.

Agglomerative clustering receives a query δ(q) and a snippet set A_(δ)as input, represented in the space where sim, merge and is_includedfunctions are defined. Initially, each snippet a∈A_(δ) is an individualcluster centroid in C. Pairwise syntactic similarity between clustercentroids is stored in a matrix S of the size |

|×|

|, the relevance similarity is stored in matrix W of the same size |

|×|

|. On each iteration the most similar cluster centroids are chosen (line1511) to compute a new centroid c, which is their shared description(line 1512). The weight of a new cluster C is the maximal relevanceweight of its members, i.e., w_(C)=max{w_(a)|δ(a)∈C}. Here we usecapital letters for clusters and lowercase letters for their centroids,i.e. C⊆A_(δ) for a cluster and c for its centroid.

To compute similarity between centroids, both syntactic and relevantsimilarities are taken into account. We use a weighted average of thesimilarities, i.e., similarity between centroids c_(i) and c_(j) isdefined as k₁s_(ij)+k₂w_(ij), where k₁,k₂∈R are coefficients ofimportance of syntactic and relevance similarities, respectively. If anewly created centroid contains the description of the original query(i.e., it retains complete information about the query) the two mergedcentroids are replaced by their shared description, the weight of thecluster is the maximal weight of the members of the merged clusters,i.e., w_(C)=max{w_(a)|δ(a)∈C_(i)∪C_(j)}. When all the centroids that donot lose the information from the original query are computed (thecentroids that include as many snippets as possible and retaininformation from the query), the subsets of snippets corresponding tothe computed centroids are returned.

Computing Similarity

Representing text as a vector. Once the snippets are received, a new setof terms from ∪{q} is computed. The N found terms correspond to thevector entries. Each text is represented by a vector of size N andfilled with 0s and 1s. The “1” at i means that the ith term is containedin the text.

1. merge(d₁,d₂)=d₁·d₂

2. sim(d₁,d₂):

-   -   (a) sim(d₁,d₂)=JaccardSimilarity(d₁,d₂)    -   (b) sim(d₁,d₂)=CosineSimilarity(d₁,d₂)    -   (c) sim(d₁,d₂)=SimpleMatchingCoefficent(d₁,d₂)

3. is_included(d₁,d₂)=d₁□d₂≡merge(d₁,d₂)=d₁

The following similarity measure is based on Parse Thickets:

1. merge(d₁,d₂)=d₁□d₂

2. sim(d₁,d₂):

sim^(max)(d₁, d₂)  :=  max_(chunk ∈ (d₁∏d₂))Score(chunk)${{sim}^{avg}\left( {d_{1},d_{2}} \right)}\mspace{14mu}\text{:=}\mspace{14mu}\frac{1}{\left( {d_{1}{\prod d_{2}}} \right)}{\sum\limits_{{chunk} \in {({d_{1}{\prod d_{2}}})}}{{Score}({chunk})}}$

3. is_included(d₁,d₂)=d₁□d₂

-   -   (c) Relevance Similarity

${rel}_{{sim}{({w_{i},w_{j}})}} = {1 - \frac{{w_{i} - w_{j}}}{\max_{i,{j \in 1},\ldots,{A}}w_{ij}}}$Virtual Social Dialogue Construction

To develop the virtual social dialogue, dialogue application 102 formsquestions and answers. Dialogue application 102 identifies, from theelectronic documents, a question and an answer that are relevant to theselected topic. The answer is in rhetorical agreement with the question.Together, the question and the answer form a virtual conversation thatcan be depicted as between one or more agents or users.

For example, to build a question from a paragraph of text, the text isdivided into elementary discourse units (EDUs). A discourse tree isformed, in which the EDUs are at the bottom level. From the EDUs,satellite EDUs are then selected as answers to questions, which arederived from these EDUs by means of generalization. The questions areinserted into the corresponding text as if someone is interrupting thespeaker in the moments of transition from nucleus to satellite EDUs.

FIG. 16 depicts an exemplary process 1600 for a construction of avirtual social dialogue, in accordance with an aspect of the presentdisclosure. Process 1600 can be implemented by dialogue application 102.

For illustrative purposes, process 1600 is discussed with respect toFIG. 17.

FIG. 17 illustrates an approach to virtual social dialogue construction,in accordance with an aspect. FIG. 17 depicts part of a discourse tree1700. Discourse tree includes various rhetorical relations andelementary discourse units. In some cases, discourse tree 1700 can be acommunicative discourse tree. Discourse tree 1700 includes satelliteEDUs 1701, 1702, and 1703 and corresponding questions 1711, 1712, and1713.

At block 1601, process 1600 involves constructing a discourse tree fromthe electronic documents. Dialogue application 102 creates discoursetree 1700. In some cases, dialogue application 102 creates a sequence ofdiscourse trees for the electronic document, a single communicativediscourse tree for every a paragraph (e.g., average 3-5 sentences).

At block 1602, process 1600 involves identifying, from the discoursetree, satellite elementary discourse units. Dialogue application 102identifies satellite EDUs 1701, 1702, and 1703. Each satelliteelementary discourse unit can represent an answer.

At block 1603, process 1600 involves identifying a sentencecorresponding to a satellite elementary discourse unit. For example, thesentence corresponding to satellite EDU 1703 is “However, theInvestigative Committee of the Russian Federation believes that theplane was hit by a missile from the air which was not produced inRussia.”

At block 1604, process 1600 involves identifying a question from thesatellite elementary discourse unit. Disclosed solutions employ one ormore techniques such as rhetorical structure theory, communicativediscourse trees, template matching, syntactic generalization, andweb-mining. For example, in an aspect, disclosed solutions userhetorical structure theory to form questions that correspond to theanswers. In a further aspect, disclosed solutions use syntacticgeneralization and other discourse techniques to generate a set ofquestion templates. The question templates can be used to verify that agenerated question is of sufficient specificity. For example, a questionshould not be too specific as to give away the answer (e.g., “What isthe name of a rock band from Liverpool, England with four members”).

a word that represents either (i) a noun, (ii) a verb, or (iii)adjective. Continuing the example, the satellite elementary discourseunit (EDU) 1703 is “which was not produced in Russia.”

At block 1605, process 1600 involves inserting the question into theelectronic document. Discourse approaches can be used to guide placementof the questions in the electronic documents.

Evaluation of Dialogue Effectiveness and Coverage

Evaluating the effectiveness of information delivery via virtual socialdialogues, we compare the traditional chatbot sessions where users weregiven plain-text answers, and the ones where users were given virtualsocial dialogues.

Results on comparative usability of conventional dialogue and virtualsocial dialogue are presented. Dialogues are assessed with respect tofollowing usability properties:

-   -   1) The speed of arriving to the sought piece of information. It        is measured as a number of iteration (a number of user        utterances) preceding the final reply of the chatbot which gave        an answer wanted by the user. We measure the number of steps        only if the user confirms that she accepts the answer.    -   2) The speed of arriving to a decision to commit a transaction        such as purchase or reservation or selection. A user is expected        to accumulate sufficient information, and this information such        as reviews should be convincing enough for making such decision.    -   3) A number of entities that were explored during a session with        the chatbot is also measured. How thorough and comprehensive the        chatbot session is of particular interest, in particular, how        much the user actually learns from it. This assessment is        sometimes opposite to the above two measures but is nevertheless        important for understanding the overall usability of various        conversational modes.

Precision and recall of search sessions with either dialogue mode arenot compared, because the same information is delivered, but in distinctmodes.

The evaluation of usability is presented in Table 1.

TABLE 1 Evaluation of comparative effectiveness of conventional andvirtual social dialogues Conventional dialogues Virtual social dialoguesCoverage Coverage # of # of # of iterations exploration # of iterationsexploration iterations till # of iterations till # of till founddecision entities till found decision entities Conventional 4.6 6.3 10.8— — — only Virtual only — — — 4.1 6.0 13.7 Conventional 4.0 5.7 7.6 6.111.3 15.1 followed by virtual Virtual 5.6 7.1 12.3 3.7 7.0 11.5 followedby conventional

In the second and third rows, we assess the stand-alone systems. One canobserve that virtual social dialogues take less iteration on average forinformation access and about the same number of iterations for decisionsas conventional dialogues do. Virtual social dialogues stimulate theuser to explore a higher number of entities though.

Notice that the bottom row, the chat scenario proceeds from right toleft. In the bottom two rows, we observe the usability of the hybridsystem. When a conventional dialogue is followed by a virtual one, alower portion of users is satisfied by the first step in comparison tothe inverse architecture, where virtual is followed by conventional.

Related Work and Conclusions

(Piwek et al 2007) were pioneers of automated construction of dialogues,proposing Text2Dialogue system. The authors provided a theoreticalfoundation of the mapping that the system performs from RST structuresto Dialogue representation structures. The authors introduced a numberof requirements for a dialogue generation system (robustness,extensibility, and variation and control) and reported on the evaluationof the mapping rules.

An important body of work concerns tutorial dialogue systems. Some ofthe work in that area focuses on authoring tools for generatingquestions, hints, and prompts. Typically, these are, however, singleutterances by a single interlocutor, rather than an entire conversationbetween two agents. Some researchers have concentrated on generatingquestions together with possible answers such as multiple choice testitems, but this work is restricted to a very specific type ofquestion-answer pairs (Mitkov et al 2006).

Conversion of a text into a dialogue is different from the dialoguegeneration problem; the former is a training set—based foundation forthe latter.

Response generation for dialogue can be viewed as a source-to-targettransduction problem. (Sordoni et al. 2015) rescores the outputs of aphrasal machine translation-based conversation system with a neuralmodel incorporating prior context. Recent progress insequence-to-sequence models has been leveraged (Luan et al., 2016) tobuild an end-to-end dialogue systems that firstly applies an utterancemessage to a distributed vector representation using an encoder, thensecondly generates a response from this representation. (Li et al. 2016)simulate dialogues between two virtual agents, using policy gradientmethods to reward sequences that display three useful conversationalproperties: informativity, coherence, and ease of answering. We measuredcomparable dialogue effectiveness properties such as the speed ofarrival to a search result, a decision and domain coverage, in thecurrent study.

Dialogue acts is an important source which differentiates between aplain text and a dialogue. Proposed algorithm of virtual socialdialogues can assist with building domain-specific chatbot trainingdatasets. Recently released dataset, DailyDialog(Li et al., 2017b), isthe only dataset that has utterances annotated with dialogue acts and islarge enough for learning conversation models. Unlike the virtual socialdialogues produced in this study, in DailyDialog conversations are nottask oriented, and each conversation focuses on one topic. Eachutterance is annotated with four dialogue acts.

We proposed a novel mode of chatbot interaction via virtual socialdialogue. It addresses sparseness of dialogue data on one hand andconvincingness, perceived authenticity of information presented viadialogues on the other hand. We quantitatively evaluated improvement ofuser satisfaction with virtual social dialogue in comparison to regularchatbot replies and confirmed the strong points of the former. Weconclude that virtual social dialogue is an important feature related tosocial search to be leveraged by a chatbot.

Example Computing Systems

FIG. 18 depicts a simplified diagram of a distributed system 1800 forimplementing one of the aspects. In the illustrated aspect, distributedsystem 1800 includes one or more client computing devices 1802, 1804,1806, and 1808, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 1810. Server 1812 may becommunicatively coupled with remote client computing devices 1802, 1804,1806, and 1808 via network 1810.

In various aspects, server 1812 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude nonvirtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some aspects, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 1802, 1804, 1806, and/or1808. Users operating client computing devices 1802, 1804, 1806, and/or1808 may in turn utilize one or more client applications to interactwith server 1812 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components1818, 1820 and 1822 of system 1800 are shown as being implemented onserver 1812. In other aspects, one or more of the components of system1800 and/or the services provided by these components may also beimplemented by one or more of the client computing devices 1802, 1804,1806, and/or 1808. Users operating the client computing devices may thenutilize one or more client applications to use the services provided bythese components. These components may be implemented in hardware,firmware, software, or combinations thereof. It should be appreciatedthat various different system configurations are possible, which may bedifferent from distributed system 1800. The aspect shown in the figureis thus one example of a distributed system for implementing an aspectsystem and is not intended to be limiting.

Client computing devices 1802, 1804, 1806, and/or 1808 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 1802, 1804,1806, and 1808 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s)1810.

Although exemplary distributed system 1800 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 1812.

Network(s) 1810 in distributed system 1800 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 1810 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 1810 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.18 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 1812 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 1812 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 1812 using software defined networking. In variousaspects, server 1812 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 1812 may correspond to a server for performingprocessing described above according to an aspect of the presentdisclosure.

Server 1812 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 1812 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 1812 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 802, 804, 806, and 808.As an example, data feeds and/or event updates may include, but are notlimited to, Twitter® feeds, Facebook® updates or real-time updatesreceived from one or more third party information sources and continuousdata streams, which may include real-time events related to sensor dataapplications, financial tickers, network performance measuring tools(e.g., network monitoring and traffic management applications),clickstream analysis tools, automobile traffic monitoring, and the like.Server 1812 may also include one or more applications to display thedata feeds and/or real-time events via one or more display devices ofclient computing devices 1802, 1804, 1806, and 1808.

Distributed system 1800 may also include one or more databases 1814 and1816. Databases 1814 and 1816 may reside in a variety of locations. Byway of example, one or more of databases 1814 and 1816 may reside on anon-transitory storage medium local to (and/or resident in) server 1812.Alternatively, databases 1814 and 1816 may be remote from server 1812and in communication with server 1812 via a network-based or dedicatedconnection. In one set of aspects, databases 1814 and 1816 may reside ina storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 1812 may be stored locallyon server 1812 and/or remotely, as appropriate. In one set of aspects,databases 1814 and 1816 may include relational databases, such asdatabases provided by Oracle, that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 19 is a simplified block diagram of one or more components of asystem environment 1900 by which services provided by one or morecomponents of an aspect system may be offered as cloud services, inaccordance with an aspect of the present disclosure. In the illustratedaspect, system environment 1900 includes one or more client computingdevices 1904, 1906, and 1908 that may be used by users to interact witha cloud infrastructure system 1902 that provides cloud services. Theclient computing devices may be configured to operate a clientapplication such as a web browser, a proprietary client application(e.g., Oracle Forms), or some other application, which may be used by auser of the client computing device to interact with cloudinfrastructure system 1902 to use services provided by cloudinfrastructure system 1902.

It should be appreciated that cloud infrastructure system 1902 depictedin the figure may have other components than those depicted. Further,the aspect shown in the figure is only one example of a cloudinfrastructure system that may incorporate an aspect of the invention.In some other aspects, cloud infrastructure system 1902 may have more orfewer components than shown in the figure, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

Client computing devices 1904, 1906, and 1908 may be devices similar tothose described above for 1802, 1804, 1806, and 1808.

Although exemplary system environment 1900 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 1902.

Network(s) 1910 may facilitate communications and exchange of databetween client computing devices 1904, 1906, and 1908 and cloudinfrastructure system 1902. Each network may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including those described above for network(s) 1810.

Cloud infrastructure system 1902 may comprise one or more computersand/or servers that may include those described above for server 1812.

In certain aspects, services provided by the cloud infrastructure systemmay include a host of services that are made available to users of thecloud infrastructure system on demand, such as online data storage andbackup solutions, Web-based e-mail services, hosted office suites anddocument collaboration services, database processing, managed technicalsupport services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain aspects, cloud infrastructure system 1902 may include a suiteof applications, middleware, and database service offerings that aredelivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Large volumes of data, sometimes referred to as big data, can be hostedand/or manipulated by the infrastructure system on many levels and atdifferent scales. Such data can include data sets that are so large andcomplex that it can be difficult to process using typical databasemanagement tools or traditional data processing applications. Forexample, terabytes of data may be difficult to store, retrieve, andprocess using personal computers or their rack-based counterparts. Suchsizes of data can be difficult to work with using most currentrelational database management systems and desktop statistics andvisualization packages. They can require massively parallel processingsoftware running thousands of server computers, beyond the structure ofcommonly used software tools, to capture, curate, manage, and processthe data within a tolerable elapsed time.

Extremely large data sets can be stored and manipulated by analysts andresearchers to visualize large amounts of data, detect trends, and/orotherwise interact with the data. Tens, hundreds, or thousands ofprocessors linked in parallel can act upon such data in order to presentit or simulate external forces on the data or what it represents. Thesedata sets can involve structured data, such as that organized in adatabase or otherwise according to a structured model, and/orunstructured data (e.g., emails, images, data blobs (binary largeobjects), web pages, complex event processing). By leveraging an abilityof an aspect to relatively quickly focus more (or fewer) computingresources upon an objective, the cloud infrastructure system may bebetter available to carry out tasks on large data sets based on demandfrom a business, government agency, research organization, privateindividual, group of like-minded individuals or organizations, or otherentity.

In various aspects, cloud infrastructure system 1902 may be adapted toautomatically provision, manage and track a customer's subscription toservices offered by cloud infrastructure system 1902. Cloudinfrastructure system 1902 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 1902 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 1902 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 1902 and the services provided by cloudinfrastructure system 1902 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some aspects, the services provided by cloud infrastructure system1902 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 1902. Cloud infrastructure system 1902 then performs processingto provide the services in the customer's subscription order.

In some aspects, the services provided by cloud infrastructure system1902 may include, without limitation, application services, platformservices and infrastructure services. In some examples, applicationservices may be provided by the cloud infrastructure system via a SaaSplatform. The SaaS platform may be configured to provide cloud servicesthat fall under the SaaS category. For example, the SaaS platform mayprovide capabilities to build and deliver a suite of on-demandapplications on an integrated development and deployment platform. TheSaaS platform may manage and control the underlying software andinfrastructure for providing the SaaS services. By utilizing theservices provided by the SaaS platform, customers can utilizeapplications executing on the cloud infrastructure system. Customers canacquire the application services without the need for customers topurchase separate licenses and support. Various different SaaS servicesmay be provided. Examples include, without limitation, services thatprovide solutions for sales performance management, enterpriseintegration, and business flexibility for large organizations.

In some aspects, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someaspects, platform services provided by the cloud infrastructure systemmay include database cloud services, middleware cloud services (e.g.,Oracle Fusion Middleware services), and Java cloud services. In oneaspect, database cloud services may support shared service deploymentmodels that enable organizations to pool database resources and offercustomers a Database as a Service in the form of a database cloud.Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain aspects, cloud infrastructure system 1902 may also includeinfrastructure resources 1930 for providing the resources used toprovide various services to customers of the cloud infrastructuresystem. In one aspect, infrastructure resources 1930 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform.

In some aspects, resources in cloud infrastructure system 1902 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 1902 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain aspects, a number of internal shared services 1932 may beprovided that are shared by different components or modules of cloudinfrastructure system 1902 and by the services provided by cloudinfrastructure system 1902. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain aspects, cloud infrastructure system 1902 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one aspect, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 1902, and the like.

In one aspect, as depicted in the figure, cloud management functionalitymay be provided by one or more modules, such as an order managementmodule 1920, an order orchestration module 1922, an order provisioningmodule 1924, an order management and monitoring module 1926, and anidentity management module 1928. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 1934, a customer using a client device, such asclient computing device 1904, 1906 or 1908, may interact with cloudinfrastructure system 1902 by requesting one or more services providedby cloud infrastructure system 1902 and placing an order for asubscription for one or more services offered by cloud infrastructuresystem 1902. In certain aspects, the customer may access a cloud UserInterface (UI), cloud UI 1912, cloud UI 1914 and/or cloud UI 1916 andplace a subscription order via these UIs. The order information receivedby cloud infrastructure system 1902 in response to the customer placingan order may include information identifying the customer and one ormore services offered by the cloud infrastructure system 1902 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 1912, 1914 and/or 1916.

At operation 1936, the order is stored in order database 1918. Orderdatabase 1918 can be one of several databases operated by cloudinfrastructure system 1902 and operated in conjunction with other systemelements.

At operation 1938, the order information is forwarded to an ordermanagement module 1920. In some instances, order management module 1920may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 1940, information regarding the order is communicated to anorder orchestration module 1922. Order orchestration module 1922 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 1922 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 1924.

In certain aspects, order orchestration module 1922 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 1942, upon receiving an order for a newsubscription, order orchestration module 1922 sends a request to orderprovisioning module 1924 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 1924 enables the allocation of resources for the services orderedby the customer. Order provisioning module 1924 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 1902 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 1922 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 1944, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientcomputing devices 1904, 1906 and/or 1908 by order provisioning module1924 of cloud infrastructure system 1902.

At operation 1946, the customer's subscription order may be managed andtracked by an order management and monitoring module 1926. In someinstances, order management and monitoring module 1926 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain aspects, cloud infrastructure system 1902 may include anidentity management module 1928. Identity management module 1928 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 1902. In someaspects, identity management module 1928 may control information aboutcustomers who wish to utilize the services provided by cloudinfrastructure system 1902. Such information can include informationthat authenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.). Identitymanagement module 1928 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 20 illustrates an exemplary computer system 2000, in which variousaspects of the present invention may be implemented. The computer system2000 may be used to implement any of the computer systems describedabove. As shown in the figure, computer system 2000 includes aprocessing unit 2004 that communicates with a number of peripheralsubsystems via a bus subsystem 2002. These peripheral subsystems mayinclude a processing acceleration unit 2006, an I/O subsystem 2008, astorage subsystem 2018 and a communications subsystem 2024. Storagesubsystem 2018 includes tangible computer-readable storage media 2022and a system memory 2010.

Bus subsystem 2002 provides a mechanism for letting the variouscomponents and subsystems of computer system 2000 communicate with eachother as intended. Although bus subsystem 2002 is shown schematically asa single bus, alternative aspects of the bus subsystem may utilizemultiple buses. Bus subsystem 2002 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P2086.1standard.

Processing unit 2004, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 2000. One or more processorsmay be included in processing unit 2004. These processors may includesingle core or multicore processors. In certain aspects, processing unit2004 may be implemented as one or more independent processing units 2032and/or 2034 with single or multicore processors included in eachprocessing unit. In other aspects, processing unit 2004 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various aspects, processing unit 2004 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processingunit 2004 and/or in storage subsystem 2018. Through suitableprogramming, processing unit 2004 can provide various functionalitiesdescribed above. Computer system 2000 may additionally include aprocessing acceleration unit 2006, which can include a digital signalprocessor (DSP), a special-purpose processor, and/or the like.

I/O subsystem 2008 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system2000 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 2000 may comprise a storage subsystem 2018 thatcomprises software elements, shown as being currently located within asystem memory 2010. System memory 2010 may store program instructionsthat are loadable and executable on processing unit 2004, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 2000, systemmemory 2010 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 2004. In some implementations, system memory 2010 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system2000, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 2010 also illustratesapplication programs 2012, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 2014, and an operating system 2016. By wayof example, operating system 2016 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 2018 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some aspects. Software (programs, codemodules, instructions) that when executed by a processor provide thefunctionality described above may be stored in storage subsystem 2018.These software modules or instructions may be executed by processingunit 2004. Storage subsystem 2018 may also provide a repository forstoring data used in accordance with the present invention.

Storage subsystem 2018 may also include a computer-readable storagemedia reader 2020 that can further be connected to computer-readablestorage media 2022. Together and, optionally, in combination with systemmemory 2010, computer-readable storage media 2022 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 2022 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computer system 2000.

By way of example, computer-readable storage media 2022 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 2022 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 2022 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 2000.

Communications subsystem 2024 provides an interface to other computersystems and networks. Communications subsystem 2024 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 2000. For example, communications subsystem 2024may enable computer system 2000 to connect to one or more devices viathe Internet. In some aspects, communications subsystem 2024 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.18 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some aspects, communications subsystem 2024 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some aspects, communications subsystem 2024 may also receive inputcommunication in the form of structured and/or unstructured data feeds2026, event streams 2028, event updates 2030, and the like on behalf ofone or more users who may use computer system 2000.

By way of example, communications subsystem 2024 may be configured toreceive unstructured data feeds 2026 in real-time from users of socialmedia networks and/or other communication services such as Twitter®feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS)feeds, and/or real-time updates from one or more third party informationsources.

Additionally, communications subsystem 2024 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 2028 of real-time events and/or event updates 2030, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 2024 may also be configured to output thestructured and/or unstructured data feeds 2026, event streams 2028,event updates 2030, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 2000.

Computer system 2000 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 2000 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious aspects.

In the foregoing specification, aspects of the invention are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, aspects can be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

What is claimed is:
 1. A method of dialogue management for an autonomousagent, the method comprising: receiving, from a user device, a searchquery comprising text fragments; obtaining a plurality of search resultsby performing a search of a plurality of electronic documents using thesearch query; generating a syntactic similarity matrix that numericallyrepresents a syntactic similarity between each of the search results;generating a relevance similarity matrix that numerically represents arelevancy between each of the search results; clustering the searchresults into clusters by identifying pairs of the search results that(i) are separated in the syntactic similarity matrix by less than afirst minimum distance and (ii) are separated in the relevancesimilarity matrix by less than a second minimum distance; forming a setof topics by identifying, for each cluster of the clusters, a nounphrase that is common between search results in the cluster; outputting,to the user device, the set of topics; receiving, from the user device,a selection of a topic from the set of topics; identifying, from theplurality of electronic documents, a question that is relevant to theselected topic, wherein the identifying comprises: constructing adiscourse tree from the plurality of electronic documents, wherein thediscourse tree comprises nodes, each terminal node of the nodesassociated with an elementary discourse unit and each nonterminal nodeof the nodes representing a relationship between two of the elementarydiscourse units; identifying, from the discourse tree, a satelliteelementary discourse unit that represents an answer; and creating, fromthe answer, a question that is in rhetorical agreement with the answer,wherein the question and the answer form a virtual dialogue; andproviding the virtual dialogue to the user device.
 2. The method ofclaim 1, wherein generating the syntactic similarity matrix comprisesdetermining, for each search result of the plurality of search results,a distance indicating similarity with each of the other search results,and wherein the first minimum distance is a minimum of the distances. 3.The method of claim 1, wherein generating the relevance similaritymatrix comprises, for each search result of the plurality of searchresults: identifying, in the search result, a set of keywords; andcalculating, for each keyword of a set of keywords, a respectivefrequency of occurrence, wherein the second minimum distance is derivedfrom the frequencies of occurrence.
 4. The method of claim 1, whereinthe clustering further comprises: iteratively, until a threshold numberof clusters are obtained: identifying a first search result and a secondsearch result that are separated by a minimum distance; and merging,into a cluster, the first search result and the second search result;and determining, for each cluster, a topic comprising a noun phrase froma search result associated with the respective cluster.
 5. The method ofclaim 1, wherein the creating comprises: identifying a sentencecorresponding to the satellite elementary discourse unit; identifying,within the satellite elementary discourse unit, a word that representseither (i) a noun, (ii) a verb, or (iii) adjective; replacing, in thesentence, the word with a question word, thereby creating a question;and forming the virtual dialogue by inserting the question immediatelypreceding the answer.
 6. The method of claim 1, further comprising:responsive to receiving, from the user device, a request to interactwith the set of topics, searching the topics for a relevant fragment oftext; and responsive to determining that the relevant fragment of textis responsive to the search query, presenting fragment of text to theuser device.
 7. The method of claim 1, further comprising: receiving,from a user device, an additional question comprising text fragments;generating, from the plurality of electronic documents, an additionalanswer, wherein the additional answer is relevant to the topic andwherein the additional answer is in rhetorical agreement with theadditional question; attributing an additional virtual actor to theadditional answer; updating the virtual dialogue with the additionalanswer; and providing the virtual dialogue to the user device.
 8. Themethod of claim 1, wherein the question and the answer are related by arhetorical relation.
 9. A non-transitory computer-readable mediumstoring computer-executable program instructions that when executed by aprocessing device, cause the processing device to perform operationscomprising: receiving, from a user device, a search query comprisingtext fragments; obtaining a plurality of search results by performing asearch of a plurality of electronic documents using the search query;generating a syntactic similarity matrix that numerically represents asyntactic similarity between each of the search results; generating arelevance similarity matrix that numerically represents a relevancybetween each of the search results; clustering the search results intoclusters by identifying pairs of the search results that (i) areseparated in the syntactic similarity matrix by less than a firstminimum distance and (ii) are separated in the relevance similaritymatrix by less than a second minimum distance; forming a set of topicsby identifying; for each cluster of the clusters, a noun phrase that iscommon between search results in the cluster; outputting, to the userdevice, the set of topics; receiving, from the user device, a selectionof a topic from the set of topics; identifying, from the plurality ofelectronic documents, a question that is relevant to the selected topic,wherein the identifying comprises: constructing a discourse tree fromthe plurality of electronic documents, wherein the discourse treecomprises nodes, each terminal node of the nodes associated with anelementary discourse unit and each nonterminal node of the nodesrepresenting a relationship between two of the elementary discourseunits; identifying, from the discourse tree, a satellite elementarydiscourse unit that represents an answer; and creating, from the answer,a question that is in rhetorical agreement with the answer, wherein thequestion and the answer form a virtual dialogue; and providing thevirtual dialogue to the user device.
 10. The non-transitorycomputer-readable medium of claim 9, wherein generating the syntacticsimilarity matrix comprises determining, for each search result of theplurality of search results, a distance indicating similarity with eachof the other search results, and wherein the first minimum distance is aminimum of the distances.
 11. The non-transitory computer-readablemedium of claim 9, wherein generating the relevance similarity matrixcomprises, for each search result of the plurality of search results:identifying, in the search result, a set of keywords; and calculating,for each keyword of a set of keywords, a respective frequency ofoccurrence, and wherein the second minimum distance is derived from thefrequencies of occurrence.
 12. The non-transitory computer-readablemedium of claim 9, wherein the clustering further comprises:iteratively, until a threshold number of clusters are obtained:identifying a first search result and a second search result that areseparated by a minimum distance; and merging, into a cluster, the firstsearch result and the second search result; and determining, for eachcluster, a topic comprising a noun phrase from a search resultassociated with the respective cluster.
 13. The non-transitorycomputer-readable medium of claim 9, wherein the creating identifyingcomprises: identifying a sentence corresponding to the satelliteelementary discourse unit; identifying, within the satellite elementarydiscourse unit, a word that represents either (i) a noun, (ii) a verb,or (iii) adjective; and replacing, in the sentence, the word with aquestion word, thereby creating a question.
 14. The non-transitorycomputer-readable medium of claim 9, the operations further comprising:responsive to receiving, from the user device, a request to interactwith the set of topics, searching the topics for a relevant fragment oftext; and responsive to determining that the relevant fragment of textis responsive to the search query, presenting fragment of text to theuser device.
 15. The non-transitory computer-readable medium of claim 9,the operations further comprising: receiving, from a user device, anadditional question comprising text fragments; generating, from theplurality of electronic documents, an additional answer, wherein theadditional answer is relevant to the topic and wherein the additionalanswer is in rhetorical agreement with the additional question;attributing an additional virtual actor to the additional answer;updating the virtual dialogue with the additional answer; and providingthe virtual dialogue to the user device.
 16. A system comprising: anon-transitory computer-readable medium storing computer-executableprogram instructions; and a processing device communicatively coupled tothe computer-readable medium for executing the computer-executableprogram instructions, wherein executing the computer-executable programinstructions configures the processing device to perform operationscomprising: receiving, from a user device, a search query comprisingtext fragments; obtaining a plurality of search results by performing asearch of a plurality of electronic documents using the search query;generating a syntactic similarity matrix that numerically represents asyntactic similarity between each of the search results; generating arelevance similarity matrix that numerically represents a relevancybetween each of the search results; clustering the search results intoclusters by identifying pairs of the search results that (i) areseparated in the syntactic similarity matrix by less than a firstminimum distance and (ii) are separated in the relevance similaritymatrix by less than a second minimum distance; forming a set of topicsby identifying, for each cluster of the clusters, a noun phrase that iscommon between search results in the cluster; outputting, to the userdevice, the set of topics; receiving, from the user device, a selectionof a topic from the set of topics; identifying, from the plurality ofelectronic documents, a question that is relevant to the selected topic,wherein the identifying comprises: constructing a discourse tree fromthe plurality of electronic documents, wherein the discourse treecomprises nodes, each terminal node of the nodes associated with anelementary discourse unit and each nonterminal node of the nodesrepresenting a relationship between two of the elementary discourseunits; identifying, from the discourse tree, a satellite elementarydiscourse unit that represents an answer; and creating, from the answer,a question that is in rhetorical agreement with the answer and thequestion and the answer form a virtual dialogue; and providing thevirtual dialogue to the user device.
 17. The system of claim 16, whereingenerating the syntactic similarity matrix comprises determining, foreach search result of the plurality of search results, a distanceindicating similarity with each of the other search results, and whereinthe first minimum distance is a minimum of the distances.
 18. The systemof claim 16, wherein generating the relevance similarity matrixcomprises, for each search result of the plurality of search results:identifying, in the search result, a set of keywords; and calculating,for each keyword of a set of keywords, a respective frequency ofoccurrence, and wherein the second minimum distance is derived from thefrequencies of occurrence.
 19. The system of claim 16, wherein theclustering further comprises: iteratively, until a threshold number ofclusters are obtained: identifying a first search result and a secondsearch result that are separated by a minimum distance; and merging,into a cluster, the first search result and the second search result;and determining, for each cluster, a topic comprising a noun phrase froma search result associated with the respective cluster.
 20. The systemof claim 16, wherein the creating comprises: identifying a sentencecorresponding to the satellite elementary discourse unit; identifying,within the satellite elementary discourse unit, a word that representseither (i) a noun, (ii) a verb, or (iii) adjective; and replacing, inthe sentence, the word with a question word, thereby creating aquestion.